Phenotyping

fyron.phenotyping builds transparent rule-based clinical phenotypes from tabular FHIR-derived or registry-style data. Use it when diagnosis codes, procedure codes, lab thresholds, or medication exposure rows need to become patient-level cohort flags.

Phenotyping rules are explicit definitions, not validated clinical truth. Keep the rule inputs, code lists, thresholds, and evidence counts with the analysis so clinicians and reviewers can inspect what happened.

For broad ICD-10 disease groups, German OPS procedure categories, delimited code parsing, and date interval columns, see DataFrame Operations. Use fyron.phenotyping when the study needs explicit rule definitions with evidence counts and patient-level flags.

Imports

python

from fyron import phenotyping as fph

Function Reference

Function	Required	Optional	Returns
`define_code_phenotype`	table, code column, codes	patient/date/code-system columns, phenotype name	patient-level phenotype table
`define_lab_threshold_phenotype`	table, value column, threshold	direction, patient/date columns, phenotype name	patient-level phenotype table
`define_medication_exposure`	table, medication column, medications	patient/start/end columns, phenotype name	patient-level exposure table
`combine_phenotypes`	phenotype tables	`strategy="any"` or `"all"`	combined patient-level table
`phenotype_summary`	phenotype table, phenotype column	patient/group columns	prevalence summary table

Code Phenotype

python

condition_flag = fph.define_code_phenotype(
    conditions,
    code_col="code",
    codes=["I50", "I50.1"],
    patient_col="patient_id",
    date_col="recorded_date",
    code_system_col="system",
    code_systems=["ICD-10"],
    phenotype_name="heart_failure",
)

Outputs include patient_id, heart_failure, evidence_count, evidence_values, and optional first_date / last_date.

Lab Threshold Phenotype

python

anemia = fph.define_lab_threshold_phenotype(
    observations,
    value_col="hemoglobin",
    threshold=12.0,
    direction="<",
    date_col="effective_datetime",
    phenotype_name="anemia",
)

Medication Exposure

python

exposure = fph.define_medication_exposure(
    medications,
    medication_col="medication_name",
    medications=["cisplatin", "carboplatin"],
    start_col="start_date",
    end_col="end_date",
    phenotype_name="platinum_exposure",
)

Combine Rules

python

case_definition = fph.combine_phenotypes(
    [condition_flag, anemia],
    strategy="any",
    phenotype_name="study_case",
)

summary = fph.phenotype_summary(
    case_definition.merge(cohort[["patient_id", "sex"]], on="patient_id"),
    phenotype_col="study_case",
    group_col="sex",
)

Common Pitfalls

Store the exact code lists used for a study.
Review date windows before interpreting first/last evidence dates.
Treat medication names/codes as site-specific unless they come from normalized terminology.
Validate rule outputs on a small manually reviewed sample before modeling.